Our recent work in [1]} developed RL algorithms for CPT-based decision making when a model of the system was not known.
We established theoretical guarantees on convergence and demonstrated that behaviors of agents using CPT-based policies closely mimicked those of a human user in the same environment.
In this paper, we focus on another defining characteristic of human behavior: a desire to keep decision-making and its outcomes hidden from external, and possibly adversarial parties.
We use the foundations and tools of differential privacy [2]}, a security property that makes it difficult for an adversary to discern information about a system by providing probabilistic guarantees on the indistinguishability of its observations.
This makes it unlikely that an adversary will learn anything of specific interest or meaningful about sensitive data [3]}.
